用于可解释零售电价预测的因果图引导时序卷积架构
A causal graph-informed temporal convolution architecture for interpretable retail electricity price forecasting
- The Ohio State University（俄亥俄州立大学）
- John Glenn College of Public Affairs, The Ohio State University（俄亥俄州立大学约翰·格伦公共事务学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出CG-TCN模型，通过整合因果图与时序卷积网络提升零售电价预测的精度与可解释性，在俄亥俄州数据上的预测误差优于基准模型，可为电力市场相关决策提供支撑。
AI中文摘要：
放松管制系统中的零售电力市场面临显著的价格波动，且与远期和期货产品存在复杂相互作用，这对有效的运营决策构成挑战。本研究提出一种因果图引导时序卷积网络（Causal Graph-Informed Temporal Convolutional Network, CG-TCN），该预测架构通过图神经嵌入将学习到的因果图整合到时序卷积网络中，以提升零售电价动态的预测精度与可解释性。首先，该架构采用多分辨率分解，从高频波动中分离出半年、季度和月度趋势；随后在这些成分及关键协变量（包括批发远期价格、零售合同属性如提前终止费）上发现因果图，同时施加保留因果方向与外生性的领域约束。学习到的因果结构被编码为邻接嵌入，用于调控TCN的卷积与注意力机制，使表示学习与因果路径对齐。基于俄亥俄州放松管制市场中十年的每日12个月固定价格住宅合同数据，研究发现批发远期价格主要决定长期零售价格趋势，而合同属性影响短期波动。CG-TCN在基准模型中表现始终更优，对每日零售电价中位数进行1步、10步、15步预测时，平均绝对百分比误差分别为3.08%、3.82%、5.43%。CG-TCN将预测性能与可解释性相结合，为竞争性电力市场的市场分析、消费者保护、监管监督、风险评估及采购规划提供透明且与政策相关的见解。
英文摘要：
Retail electricity markets in deregulated systems face significant price volatility and complex interactions with forward and futures products, posing challenges for effective operational decision-making. This study introduces a Causal Graph-Informed Temporal Convolutional Network (CG-TCN), a forecasting architecture that integrates a learned causal graph into a temporal convolutional network via a graph-neural embedding to enhance both forecasting accuracy and interpretability of retail electricity price dynamics. It first applies a multi-resolution decomposition to isolate semiannual, quarterly, and monthly trends from high-frequency fluctuations. A causal graph is then discovered over these components and key covariates, including wholesale forward prices and retail contract attributes such as early termination fees, with domain constraints that preserve causal directionality and exogeneity. The learned causal structure is encoded as an adjacency embedding that conditions the TCN's convolutions and attention, aligning representation learning with causal pathways. Using ten years of daily 12-month fixed-price residential contracts from Ohio's deregulated market, we find that wholesale forward prices primarily determine long-term retail price trends, whereas contract attributes influence short-term fluctuations. CG-TCN consistently outperforms benchmark models, achieving mean absolute percentage errors of 3.08%, 3.82%, and 5.43% for one-, ten-, and fifteen-step-ahead forecasts of daily retail electricity median prices, respectively. By combining predictive performance with interpretability, CG-TCN provides transparent, policy-relevant insight to support market analytics, consumer protection, regulatory oversight, risk assessment and procurement planning in competitive electricity markets.
